Existing speech- and speaker-recognition technology is typically based on a feature space related to a cepstrum. A cepstrum may result from taking an inverse Fourier transform (IFT) of the logarithm of the power spectrum of a signal. There may be a complex cepstrum, a real cepstrum, a power cepstrum, and/or phase cepstrum. The power cepstrum in particular finds applications in the analysis of human speech, essentially as a smoothed energy profile reflecting the power spectrum without the peaks. Feature vectors may contain values of the power cepstrum at discrete points. Occasionally feature vectors may be extended with a pitch estimate to enhance speaker-specific information. In such cases, pitch may be referred to as a “prosodic” feature, meaning it conditions or nuances the speech. Ironically, if the pitch was known with any accuracy, cepstral features may generally not be used in the first place because harmonic amplitudes would have been used instead. The set of complex harmonic amplitudes may contain most of the information in a voice. The cepstral profile may be described as a crude approximation of this set of amplitudes. But to know the amplitudes, generally speaking, the harmonic frequencies must be known, which means the pitch must be known. The prosodic pitch estimates appended to cepstral vectors may have nowhere near the precision needed to specify the harmonic frequencies.